The technical field concerns image processing. More particularly, the technical field covers processing a magnified image of biological material to identify one or more components of an object in the image. In addition, the technical field includes a combination of reagents to enhance the visualization of one or more components of an object in a magnified image of stimulated biological material and an automated process adapted to identify the components.
Biological material may include cells disposed in or on plastic or glass culture dishes, cells disposed on plastic or glass slides, and/or tissue sections mounted to plastic or glass slides.
Measurements of object features in magnified images of biological material are of increasing importance in the analysis of biological processes in automated high throughput screening (HTS) and in high content screening (HCS). For example, in HTS procedures developed for assaying cellular activity, each well of a two dimensional array of wells (in industry-standard multi-well dishes, slides, or chamber slides), contains cells that are exposed to a stimulus for some period of time (a “reaction period”) during which the cells respond to the stimulus. Images are then generated from the cells in each well, by photomicroscopy and information respecting the response is obtained from the images and analyzed to evaluate the response.
In support of extracting information from cells, stain is applied to the cells to make certain features visually more distinct than others. A stain seeks and colors a particular element or material in the cells (stains are also called dyes). Following the reaction period, information about the cells' response to the stimulus may be evident by detecting shapes, locations, dimensions and quantities of stained material in the stimulated cells. Stain may be applied before and/or after stimulation by means of materials that may include, without limitation, chemical stains and/or antibodies. More than one stain may be applied, each to enhance visibility of a particular cellular feature. For example, an antibody fused with a fluorescent molecule may be transported into cytoplasm and bound to a specific enzyme presumed to be responsive to an applied stimulus. The stain enhances the visibility of that enzyme when the cellular material is subjected to an illumination that causes the molecule to fluoresce. Another stain having an affinity for cell nuclei may be applied to make cell nuclei visible at a certain illumination wavelength. Magnified images of the cells are obtained through a microscope and captured by a camera mounted to the microscope. The microscope is operated either automatically or manually to scan the array of wells and the camera takes one or more images of the stimulated, stained cells in each well. The images are passed to an automated image process that derives information from the images, based on the locations of the stained enzyme and nuclei. The information is processed to obtain parameter values that may be combined by a function to provide one or more measurements of the reaction.
Therefore, a step of staining biological material includes applying one or more reagents to the biological material that enhance visibility of components of the material when the stained material is illuminated at certain wavelengths or wavebands. Each stain is designed to be absorbed by a particular component of interest in order to enhance the visibility of that component when the stained material is illuminated. Staining helps an automated image process to “see” and distinguish these different structures in order to accurately measure one or more responses of the visualized biological material
Images of the stained cells may be obtained while the cells are live, or after the cells have been chemically preserved with formaldehyde, methanol or other fixatives. In this regard, a step of fixing activated biological material includes applying one or more reagents to the material to stop the stimulated activity and lock the structure of the activated material against further change.
A measurement made by an automated image process requires that an image be reliably presented in a form that is manifest to the process. The image process does not perceive an image in the same way a human does. Instead, it discerns the image as an N×M array of picture elements (“pixels”), each constituted of a numerical representation of light intensity. An image includes one or more objects and may or may not include a background containing pixels of a certain intensity that contrasts with the intensity or intensities of the object pixels. An image composed of pixels defined on an array is referred to as a digital image or a digital picture. A digital image may be presented to a viewer on the screen of a display device such as a cathode ray tube (CRT), a flat panel display, a camera, or other equivalent device.
One difficult problem in the visualization of cell structures by automated image processes arises when the structures touch or overlap. For example, the analysis of cell membrane structure following activation of protein kinase C alpha (PKCα) requires that an automated image process be able to identify and distinguish the membranes whereto the PKCα migrates when stimulated in order to distinguish one cell from another. However, in cases where the cellular material is densely packed and/or agglomerated, an automated image process may have difficulty distinguishing cells whose membranes overlap and/or touch. The process may interpret a membrane boundary shared by adjacent cells as the membrane of one cell but may fail to identify an abutting cell sharing the membrane boundary. As a result, cells in an image may not be identified by the automated image process. The failure of the process to identify and/or count multiple cells with abutting boundaries may lead to deficiencies and inaccuracies in measurement of the activity of interest.
A further problem arises from the different cellular components that might be imaged for different responses or in cells with different structures. In this regard, membranes and nuclei are typically features of interest in responses that activate the membranes of many cells. A number of different proteins and other molecules may be stimulated to change location from the interior of a cell to its membrane. Among these are, for example, PKCs. Other substances may be stimulated to leave the cell membrane. Among these substances are, for example, cadherins. In other instances, substances may be stimulated to exchange locations at the membrane with other substances. A response causing transposition of substances to and/or from the membrane of a cell may be classed as a “membrane activation” response. Information about the cell's response may be obtained from an image of the membrane. When the transposing substances are stained, the cell membrane changes in visibility as transposition progresses. The outline of a cell's membrane is a closed trace that has no regular shape. Sometimes, the closed, irregularly shaped trace is called a “ring”. An image processing algorithm tailored to enhancement of irregularly-shaped rings is considered to be adapted to classification of reactions in which the cell membrane is the transposition site. Such reactions may be called membrane activation responses. An image processing algorithm for analyzing images of cells undergoing membrane activation may be termed a membrane activation image processing algorithm. However a membrane activation algorithm may not be effective in enhancing the visualization of lipid droplets within cells. As is known, adipocytes (the cell type that composes fat tissue) feature lipid droplets within the cells that represent the main storage depot of fat. Similar lipid droplets are also found in other cell types, including but not limited to muscle cells, 3T3L1 and HeLa cells, and Chinese Hamster Ovary cells. Lipid droplets are spherically-shaped, lipid-containing objects whose outlines are not effectively enhanced by algorithms adapted for the irregularly-shaped rings. Instead, an image processing algorithm adapted to enhance the regular circular outline of lipid droplets would be useful in supporting measurements of cells to responses related to lipid metabolism. Such an algorithm may be called a lipid droplet image processing algorithm.